This work is related to two areas of the existing literature: neural source code generation and model personalization. Neural code generation has generated an intense recent interest in NLP, using Transformer models [1]} in particular for code completion [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, code synthesis from examples [8]}, natural language to code [4]}, [8]}, [11]}, code feature summarization [12]}, [13]}, [14]}, [15]}, [16]}, [17]}, code search [18]}, [19]}, unit test generation [20]} and even bug fixing [21]} and detection [22]}. This paper naturally is an extension and evaluation of personalized unit test generations as studied by [20]}, and an important contribution to the understanding optimization in a deployment scenario.
